{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# 1、生成六个班的成绩\n",
    "score1 = np.random.randint(0,151,size = (50,3))\n",
    "score2 = np.random.randint(0,151,size = (50,3))\n",
    "score3 = np.random.randint(0,151,size = (50,3))\n",
    "score4 = np.random.randint(0,151,size = (50,3))\n",
    "score5 = np.random.randint(0,151,size = (50,3))\n",
    "score6 = np.random.randint(0,151,size = (50,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       [ 34, 113,  34],\n",
       "       [ 77, 118, 103],\n",
       "       [101,  54, 103],\n",
       "       [ 12,  44,  72],\n",
       "       [ 47,   0, 114],\n",
       "       [104, 132, 112],\n",
       "       [ 86,  76,  90],\n",
       "       [129, 132,  67],\n",
       "       [128, 122,  39],\n",
       "       [ 90,  18,  64],\n",
       "       [ 70,  11, 146],\n",
       "       [ 46,  10,  95],\n",
       "       [ 97,  24,  75],\n",
       "       [ 88, 120, 113],\n",
       "       [ 62,  72,  54],\n",
       "       [ 71,  91, 125],\n",
       "       [ 48,  18,  50],\n",
       "       [ 47,  28,  48],\n",
       "       [143,  66, 138],\n",
       "       [ 20,  14,  52],\n",
       "       [ 66,  62, 102],\n",
       "       [ 76,  30,  46],\n",
       "       [ 66, 116,   8],\n",
       "       [ 61, 119,   1]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2、六个班成绩合并\n",
    "score = np.concatenate([score1,score2,score3,score4,score5,score6])\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3、生成性别，水平叠加\n",
    "sex = np.random.randint(0,2,size = (300,1))\n",
    "data = np.hstack((sex,score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 4, 0, 0])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([  0, 150, 150, 150])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.        , 77.56164384, 69.41780822, 79.26712329])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 0. , 74. , 69.5, 80. ])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.        , 41.60446048, 42.37564512, 41.74798676])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4、计算男生各科成绩数学统计指标\n",
    "cond = data[:,0] == 0 # 男生\n",
    "score_min = data[cond].min(axis = 0)\n",
    "score_max = data[cond].max(axis = 0)\n",
    "score_mean = data[cond].mean(axis = 0)\n",
    "score_median = np.median(data[cond],axis = 0)\n",
    "score_std = data[cond].std(axis = 0)\n",
    "display(score_min,score_max,score_mean,score_median,score_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([  1, 148, 150, 150])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1.        , 79.96103896, 77.07792208, 77.50649351])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 1., 78., 77., 77.])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.        , 42.26291904, 45.20437464, 43.35690702])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算女生各科成绩数学统计指标\n",
    "cond = data[:,0] == 1 # 女生\n",
    "score_min = data[cond].min(axis = 0)\n",
    "score_max = data[cond].max(axis = 0)\n",
    "score_mean = data[cond].mean(axis = 0)\n",
    "score_median = np.median(data[cond],axis = 0)\n",
    "score_std = data[cond].std(axis = 0)\n",
    "display(score_min,score_max,score_mean,score_median,score_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [  0, 104, 104, 130],\n",
       "       [  0, 130,  97,  44],\n",
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       "       [  0,  60,  62,   6],\n",
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       "       [  0,  41,  21,   9],\n",
       "       [  0, 126, 150,  54],\n",
       "       [  0, 131,  94,  61],\n",
       "       [  0,   9, 129,  12],\n",
       "       [  0, 118,  91, 141],\n",
       "       [  0,  93, 125,  56],\n",
       "       [  0,   5, 139,  57],\n",
       "       [  0, 149,  81, 111],\n",
       "       [  0,  39, 124,  60],\n",
       "       [  0,  98,  41,  59],\n",
       "       [  0,  49,  94,  19],\n",
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       "       [  0,  87,   7,  77],\n",
       "       [  0,  53,  55, 117],\n",
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       "       [  0,  76,  30,  46],\n",
       "       [  0,  61, 119,   1]])"
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data[data[:,0] == 0]"
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  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "j = []\n",
    "for  i in data:\n",
    "    if i[0]==0:\n",
    "        j.append(list(i))\n"
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  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "j = np.array(j)"
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  {
   "cell_type": "code",
   "execution_count": 22,
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     "execution_count": 22,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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